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This paper describes Meta's TestGen-LLM tool, which uses LLMs to automatically improve existing human-written tests.
Search Based Software Engineering
Mark Harman and Bryan F. Jones. 2001 · 2001
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Toward the Next Generation of Recommender Systems: A Survey of the State-of-the-Art and Possible Extensions
Adomavicius and Tuzhilin. 2005 · 2005
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Why Source Code Analysis and Manipulation Will Always Be Important (Keynote Paper). In 10 t h 10^{th} IEEE International Working Conference on Source Code Analysis and Manipulation . Timisoara, Romania
Mark Harman. 2010 · 2010
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An Analysis and Survey of the Development of Mutation Testing
Yue Jia and Mark Harman. 2011 · 2011
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Search Based Software Engineering: Trends, Techniques and Applications
Mark Harman, Afshin Mansouri, and Yuanyuan Zhang. 2012 · 2012
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Regression Testing Minimisation, Selection and Prioritisation: A Survey
Shin Yoo and Mark Harman. 2012 · 2012
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Current Challenges in Automatic Software Repair
Claire Le Goues, Stephanie Forrest, and Westley Weimer. 2013 · 2013
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A large-scale evaluation of automated unit test generation using evosuite
Gordon Fraser and Andrea Arcuri. 2014 · 2014
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An empirical analysis of flaky tests. In 22 n d 22^{nd} International Symposium on Foundations of Software Engineering (FSE 2014) , Shing-Chi Cheung, Alessandro Orso, and Margaret-Anne Storey (Eds.). ACM, Hong Kong, China, 643–653
Qingzhou Luo, Farah Hariri, Lamyaa Eloussi, and Darko Marinov. 2014 · 2014
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The Oracle Problem in Software Testing: A Survey
Earl T. Barr, Mark Harman, Phil McMinn, Muzammil Shahbaz, and Shin Yoo. 2015 · 2015
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Optimising Existing Software with Genetic Programming
William B. Langdon and Mark Harman. 2015 · 2015
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An empirical study on mutation, statement and branch coverage fault revelation that avoids the unreliable clean program assumption. In Proceedings of the 39th International Conference on Software Engineering, ICSE 2017, Buenos Aires, Argentina, May 20-28, 2017 . 597–608
Thierry Titcheu Chekam, Mike Papadakis, Yves Le Traon, and Mark Harman. 2017 · 2017
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Genetic Improvement of Software: a Comprehensive Survey
Justyna Petke, Saemundur O. Haraldsson, Mark Harman, William B. Langdon, David R. White, and John R. Woodward. 2018 · 2017
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Deploying Search Based Software Engineering with Sapienz at Facebook (keynote paper). In 10 t h 10^{th} International Symposium on Search Based Software Engineering (SSBSE 2018) . Montpellier, France, 3–45
Nadia Alshahwan, Xinbo Gao, Mark Harman, Yue Jia, Ke Mao, Alexander Mols, Taijin Tei, and Ilya Zorin. 2018 · 2018
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From Start-ups to Scale-ups: Opportunities and Open Problems for Static and Dynamic Program Analysis (keynote paper). In 18 t h 18^{th} IEEE International Working Conference on Source Code Analysis and Manipulation (SCAM 2018) . Madrid, Spain, 1–23
Mark Harman and Peter O’Hearn. 2018 · 2018
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How to Design a Program Repair Bot? Insights from the Repairnator Project. In 40th International Conference on Software Engineering, Software Engineering in Practice track (ICSE 2018 SEIP track) . 1–10
Simon Urli, Zhongxing Yu, Lionel Seinturier, and Martin Monperrus. 2018 · 2018
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Automated program repair
Claire Le Goues, Michael Pradel, and Abhik Roychoudhury. 2019 · 2019
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SapFix: Automated End-to-End Repair at Scale. In International Conference on Software Engineering (ICSE) Software Engineering in Practice (SEIP) track . Montreal, Canada
Alexandru Marginean, Johannes Bader, Satish Chandra, Mark Harman, Yue Jia, Ke Mao, Alexander Mols, and Andrew Scott. 2019 · 2019
Cited alongside, same era.
A survey on ensemble learning
Xibin Dong, Zhiwen Yu, Wenming Cao, Yifan Shi, and Qianli Ma. 2020 · 2020
Cited alongside, same era.
A Survey on the Evaluation of Clone Detection Performance and Benchmarking
Jeffrey Svajlenko and Chanchal K Roy. 2020 · 2020
Cited alongside, same era.
Testing Web Enabled Simulation at Scale Using Metamorphic Testing. In International Conference on Software Engineering (ICSE) Software Engineering in Practice (SEIP) track . Virtual
John Ahlgren, Maria Eugenia Berezin, Kinga Bojarczuk, Elena Dulskyte, Inna Dvortsova, Johann George, Natalija Gucevska, Mark Harman, Maria Lomeli, Erik Meijer, Silvia Sapora, and Justin Spahr-Summers. 2021 · 2021
Cited alongside, same era.
CODAMOSA: Escaping Coverage Plateaus in Test Generation with Pre-trained Large Language Models
Caroline Lemieux, Jeevana Priya Inala, Shuvendu K Lahiri, and Siddhartha Sen. 2023 · 2023
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Assisting static analysis with large language models: A ChatGPT experiment. In Proceedings of the 31st ACM Joint European Software Engineering Conference and Symposium on the Foundations of Software Engineering . 2107–2111
Haonan Li, Yu Hao, Yizhuo Zhai, and Zhiyun Qian. 2023 · 2023
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The Scope of ChatGPT in Software Engineering: A Thorough Investigation
Wei Ma, Shangqing Liu, Wenhan Wang, Qiang Hu, Ye Liu, Cen Zhang, Liming Nie, and Yang Liu. 2023 · 2023
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Experiences from Using Code Explanations Generated by Large Language Models in a Web Software Development E-Book. In Proceedings of the 54th ACM Technical Symposium on Computer Science Education V. 1 . ACM, Toronto ON Canada, 931–937
Stephen MacNeil, Andrew Tran, Arto Hellas, Joanne Kim, Sami Sarsa, Paul Denny, Seth Bernstein, and Juho Leinonen. 2023 · 2023
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What it would take to use mutation testing in industry—a study at Facebook. In 2021 IEEE/ACM 43rd International Conference on Software Engineering: Software Engineering in Practice (ICSE-SEIP) . IEEE, 268–277
Moritz Beller, Chu-Pan Wong, Johannes Bader, Andrew Scott, Mateusz Machalica, Satish Chandra, and Erik Meijer. 2021 · 2021
Cited alongside, same era.
Evaluating Large Language Models Trained on Code
Mark Chen et al. 2021 · 2021
Cited alongside, same era.
FlakiMe: Laboratory-Controlled Test Flakiness Impact Assessment. In 44th IEEE/ACM 44th International Conference on Software Engineering, ICSE 2022, Pittsburgh, PA, USA, May 25-27, 2022 . ACM, 982–994
Maxime Cordy, Renaud Rwemalika, Adriano Franci, Mike Papadakis, and Mark Harman. 2022 · 2022
Cited alongside, same era.
Teachers are on alert for inevitable cheating after release of ChatGPT
Laura Meckler and Pranshu Verma. 2022 · 2022
Cited alongside, same era.
Overview of Sankey flow diagrams: focusing on symptom trajectories in older adults with advanced cancer
Ethan Otto, Eva Culakova, Sixu Meng, Zhihong Zhang, Huiwen Xu, Supriya Mohile, and Marie A Flannery. 2022 · 2022
Cited alongside, same era.
Automatic Generation of Programming Exercises and Code Explanations Using Large Language Models. In Proceedings of the 2022 ACM Conference on International Computing Education Research V.1 . ACM, Lugano and Virtual Event Switzerland, 27–43
Sami Sarsa, Paul Denny, Arto Hellas, and Juho Leinonen. 2022 · 2022
Cited alongside, same era.
Improving Few-Shot Prompts with Relevant Static Analysis Products
Toufique Ahmed, Kunal Suresh Pai, Premkumar Devanbu, and Earl T. Barr. 2023 · 2023
Cited alongside, same era.
Software Testing Research Challenges: An Industrial Perspective. In 2023 IEEE Conference on Software Testing, Verification and Validation (ICST 2023) . IEEE, 1–10
Nadia Alshahwan, Mark Harman, and Alexandru Marginean. 2023 · 2023
Cited alongside, same era.
Later among the works it cites.
Coffee: Boost Your Code LLMs by Fixing Bugs with Feedback
Seungjun Moon, Yongho Song, Hyungjoo Chae, Dongjin Kang, Taeyoon Kwon, Kai Tzu-iunn Ong, Seung-won Hwang, and Jinyoung Yeo. 2023 · 2023
Later among the works it cites.
Effective Test Generation Using Pre-trained Large Language Models and Mutation Testing
Arghavan Moradi Dakhel, Amin Nikanjam, Vahid Majdinasab, Foutse Khomh, and Michel C Desmarais. 2023 · 2023
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Leveraging Static Analysis for Bug Repair
Ruba Mutasim, Gabriel Synnaeve, David Pichardie, and Baptiste Rozière. 2023 · 2023
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Learning Deep Semantics for Test Completion
Pengyu Nie, Rahul Banerjee, Junyi Jessy Li, Raymond J. Mooney, and Milos Gligoric. 2023 · 2023
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Chatbots As Fluent Polyglots: Revisiting Breakthrough Code Snippets
David Noever and Kevin Williams. 2023 · 2023
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Code Llama: Open Foundation Models for Code
Baptiste Rozière, Jonas Gehring, Fabian Gloeckle, Sten Sootla, Itai Gat, Xiaoqing Ellen Tan, Yossi Adi, Jingyu Liu, Tal Remez, Jérémy Rapin, Artyom Kozhevnikov, Ivan Evtimov, Joanna Bitton, Manish Bhatt, Cristian Canton Ferrer, Aaron Grattafiori, Wenhan Xiong, Alexandre Défossez, Jade Copet, Faisal Azhar, Hugo Touvron, Louis Martin, Nicolas Usunier, Thomas Scialom, and Gabriel Synnaeve. 2023 · 2023
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Adaptive Test Generation Using a Large Language Model
Max Schäfer, Sarah Nadi, Aryaz Eghbali, and Frank Tip. 2023 · 2023
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Exploring the Effectiveness of Large Language Models in Generating Unit Tests
Mohammed Latif Siddiq, Joanna C. S. Santos, Ridwanul Hasan Tanvir, Noshin Ulfat, Fahmid Al Rifat, and Vinicius Carvalho Lopes. 2023 · 2023
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LLaMA: Open and Efficient Foundation Language Models
Hugo Touvron, Thibaut Lavril, Gautier Izacard, Xavier Martinet, Marie-Anne Lachaux, Timothée Lacroix, Baptiste Rozière, Naman Goyal, Eric Hambro, Faisal Azhar, Aurelien Rodriguez, Armand Joulin, Edouard Grave, and Guillaume Lample. 2023 · 2023
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Software Testing with Large Language Model: Survey, Landscape, and Vision
Junjie Wang, Yuchao Huang, Chunyang Chen, Zhe Liu, Song Wang, and Qing Wang. 2023 · 2023
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Universal Fuzzing via Large Language Models
Chunqiu Steven Xia, Matteo Paltenghi, Jia Le Tian, Michael Pradel, and Lingming Zhang. 2023 · 2023
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No More Manual Tests? Evaluating and Improving ChatGPT for Unit Test Generation
Zhiqiang Yuan, Yiling Lou, Mingwei Liu, Shiji Ding, Kaixin Wang, Yixuan Chen, and Xin Peng. 2023 · 2023
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Assured LLM-Based Software Engineering (keynote paper). In 2 n d . 2^{nd.} ICSE workshop on Interoperability and Robustness of Neural Software Engineering (InteNSE) (Lisbon, Portugal)
Nadia Alshahwan, Mark Harman, Alexandru Marginean, Shubho Sengupta, and Eddy Wang. 2024 · 2024
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